Abstract [en]

Many industrial problems in various fields, such as logistics, process management, orproduct design, can be formalized and expressed as optimization problems in order tomake them solvable by optimization algorithms. However, solvers that guarantee thefinding of optimal solutions (complete) can in practice be unacceptably slow. Thisis one of the reasons why approximative (incomplete) algorithms, producing near-optimal solutions under restrictions (most dominant time), are of vital importance.

Those approximative algorithms go under the umbrella term metaheuristics, each of which is more or less suitable for particular optimization problems. These algorithmsare flexible solvers that only require a representation for solutions and an evaluation function when searching the solution space for optimality.What all metaheuristics have in common is that their search is guided by certain control parameters. These parameters have to be manually set by the user andare generally problem and interdependent: A setting producing near-optimal resultsfor one problem is likely to perform worse for another. Automating the parameter setting process in a sophisticated, computationally cheap, and statistically reliable way is challenging and a significant amount of attention in the artificial intelligence and operational research communities. This activity has not yet produced any major breakthroughs concerning the utilization of problem instance knowledge or the employment of dynamic algorithm configuration.

The thesis promotes automated parameter optimization with reference to the inverse impact of problem instance diversity on the quality of parameter settings with respect to instance-algorithm pairs. It further emphasizes the similarities between static and dynamic algorithm configuration and related problems in order to show how they relate to each other. It further proposes two frameworks for instance-based algorithm configuration and evaluates the experimental results. The first is a recommender system for static configurations, combining experimental design and machine learning. The second framework can be used for static or dynamic configuration,taking advantage of the iterative nature of population-based algorithms, which is a very important sub-class of metaheuristics.

A straightforward implementation of framework one did not result in the expected improvements, supposedly because of pre-stabilization issues. The second approach shows competitive results in the scenario when compared to a state-of-the-art model-free configurator, reducing the training time by in excess of two orders of magnitude.

Abstract [en]

Parameter tuning is an optimization problem with the objective of finding good static pa-rameter settings before the execution of a metaheuristic on a problem at hand. The requirementof tuning multiple control parameters, combined with the stochastic nature of the algorithms,make parameter tuning a non-trivial problem. To make things worse, one parameter vector allowing the algorithm to solve all optimization problems to the best of its potential is verifiable non-existent, as can be inferred from the no free lunch theorem of optimization. Manual tuning can be conducted, with the drawback of being very time consuming and failure prone. Hence, means for automated parameter tuning are required. This paper serves as an overview about recent work within the field of automated parameter tuning.

Keyword

Parameter tuning, metaheuristics, optimization

National Category

Computer Sciences

Identifiers

urn:nbn:se:miun:diva-12173 (URN)

Conference

Proceedings of the 19th Annual Conference of Doctoral Students - WDS 2010

Abstract [en]

In this paper, a framework for the simplification andstandardization of metaheuristic related parameter tuning by applyinga four phase methodology, utilizing Design of Experiments andArtificial Neural Networks, is presented. Metaheuristics are multipurposeproblem solvers that are utilized on computational optimizationproblems for which no efficient problem-specific algorithmexists. Their successful application to concrete problems requires thefinding of a good initial parameter setting, which is a tedious andtime-consuming task. Recent research reveals the lack of approachwhen it comes to this so called parameter tuning process. In themajority of publications, researchers do have a weak motivation fortheir respective choices, if any. Because initial parameter settingshave a significant impact on the solutions quality, this course ofaction could lead to suboptimal experimental results, and therebya fraudulent basis for the drawing of conclusions.

Abstract [en]

That there is no best initial parameter setting for a metaheuristicon all optimization problems is a proven fact (nofree lunch theorem). This paper studies the applicability ofso called robust parameter settings for combinatorial optimizationproblems. Design of Experiments supported parameterscreening had been carried out, analyzing a discreteParticle Swarm Optimization algorithm on three demographicallyvery dissimilar instances of the Traveling SalesmenProblem. First experimental results indicate that parametersettings produce varying performance quality forthe three instances. The robust parameter setting is outperformedin two out of three cases. The results are evensignicantly worse when considering quality/time trade-o.A methodology for problem generalization is referred to asa possible solution.

Abstract [en]

Adjusting the control parameters of population-based algorithms is a means for improving the quality of these algorithms' result when solving optimization problems. The difficulty lies in determining when to assign individual values to specific parameters during the run. This paper investigates the possible implications of a generic and computationally cheap approach towards parameter analysis for population-based algorithms. The effect of parameter settings was analyzed in the application of a genetic algorithm to a set of traveling salesman problem instances. The findings suggest that statistics about local changes of a search from iteration i to iteration i + 1 can provide valuable insight into the sensitivity of the algorithm to parameter values. A simple method for choosing static parameter settings has been shown to recommend settings competitive to those extracted from a state-of-the-art parameter tuner, paramlLS, with major time and setup advantages.